function y_cum = cum2x (x,y, maxlag, nsamp, overlap, flag)
％CUM2X Cross-covariance
％ y_cum = cum2x (x,y,maxlag, samp_seg, overlap, flag)
％ x,y - data vectors/matrices with identical dimensions
％ if x,y are matrices, rather than vectors, columns are
％ assumed to correspond to independent realizations,
％ overlap is set to 0, and samp_seg to the row dimension.
％ maxlag - maximum lag to be computed [default = 0]
％ samp_seg - samples per segment [default = data_length]
％ overlap - percentage overlap of segments [default = 0]
％ overlap is clipped to the allowed range of [0,99].
Input
The input consists of two lines. The first line contains two integers n and k which are the lengths of the array and the sliding window. There are n integers in the second line.
Output
There are two lines in the output. The first line gives the minimum values in the window at each position, from left to right, respectively. The second line gives the maximum values.
Sample Input
8 3
1 3 -1 -3 5 3 6 7
Sample Output
-1 -3 -3 -3 3 3
3 3 5 5 6 7
A one-dimensional calibration object consists of three or more collinear points with known relative positions.
It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates
around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general
rigid motions synchronously, the camera set can be linearly calibrated. A linear algorithm for the camera set calibration
is proposed，and then the linear estimation is further re¯ ned using the maximum likelihood criteria. The simulated and
real image experiments show that the proposed algorithm is valid and robust.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This directory builds the Tape class driver for Microsoft® Windows® Server 2003. The class driver implements device-independent support, and exports support routines for device-specific tape miniclass drivers. It handles device-independent tape requests and calls the tape minidriver routines to process device-specific functions. Class driver splits transfer requests, when necessary, to fit the maximum transfer size for the underlying host bus adapter. It also provides device-independent, tape-specific error handling, and calls the tape miniclass driver s device-specific error handling routines.
This program compress and recostruct using wavelets. We can select level of decomposition(here maximum 4 levels are given) of images using selected wavelet.
For eg:-wavelets can be haar, db1, db2,dmey...............
Decomposition can be viewed in figure.
(Please note that select 256X256 image for better result.)
Then compression can performed,
PERFL2 give compression score.
Then reconstruction can be performed.
Each decompsition we can choose different threshold values.
For each threshold value we can calculate mse,psnr,pq(picture quality),
bit ratio etc. To get pq install pqs function .
Using Jacobi method and Gauss-Seidel iterative methods to solve the following system
The required precision is =0.00001, and the maximum iteration number N=25. Compare the number of iterations and the convergence of these two methods
